In [ ]:
 
In [107]:
#import necessary packages
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.cm as cm
import seaborn as sns
import numpy as np

import scanpy.api as sc

import scipy
scipy.__version__
Out[107]:
'1.2.2'
In [ ]:
 
In [1198]:
#designate working directory 
#path should contain: matrix.mtx, plate_map.csv, cell_annotate.txt
#for scanpy annotations: barcodes.tsv, genes.tsv
#
#

#path='/Users/derekbogdanoff/projects/XYZeq/data/20190319_miSeq/human_mouse_gene_count/'
#path="./data/20190701/"
#path="./data/20190828/"
path="./data/20190917/"
In [1203]:
array_columns=['gene','cell','count']

count_matrix=pd.read_csv(path + 'matrix.mtx', sep=' ', header=None, index_col=None, skiprows=3, names=array_columns)

#check the format of the data
count_matrix.head()
Out[1203]:
gene cell count
0 100629 1 1
1 100709 1 2
2 101247 1 1
3 101961 1 1
4 101971 1 3
In [1200]:
##show some metrics for the data 

total_UMIS=count_matrix['count'].sum()
cell_number=count_matrix['cell'].nunique()


print("number of UMIs total:", total_UMIS)
print("number of cells:", cell_number)

plt.rcParams['figure.figsize'] = [5, 5]

#UMIs per cell
UMIs=count_matrix.groupby('cell')['count'].sum()

q=UMIs.quantile(.95)
UMIs=UMIs[UMIs < q]

ax=sns.violinplot(UMIs, orient='v', color='#9467bd')
ax.set(ylabel='Transcripts', xlabel='All Cells Pre-filtering')
plt.grid(True)
plt.show()

#genes per cell
genes=count_matrix.groupby('cell')['gene'].nunique()

q=genes.quantile(0.95)
genes=genes[genes < q]


ax=sns.violinplot(genes, orient='v', color='#9467bd')
ax.set(ylabel='Genes', xlabel='All Cells Pre-filtering')
plt.grid(True)
plt.show()



#make UMI knee plot

counts=UMIs.reset_index()
counts=counts.sort_values(by=['count'], axis=0, ascending=True)
counts=counts.reset_index()
counts=counts.drop('index',axis=1)
counts=counts.reset_index()
counts.columns=['barcodes','cell','UMIs/cell']
plt.rcParams['figure.figsize'] = [13, 8]
counts.plot.scatter(x='UMIs/cell',y='barcodes')
number of UMIs total: 3908220.0
number of cells: 3021
Out[1200]:
<matplotlib.axes._subplots.AxesSubplot at 0x156f9f7b8>
In [1227]:
pwd
Out[1227]:
'/Users/derekbogdanoff/projects/XYZeq'
In [1208]:
cd ./scripts/
/Users/derekbogdanoff/projects/XYZeq/scripts
In [1113]:
from hex_spatial_map import plot_spatial_array
In [1214]:
cd ..
/Users/derekbogdanoff/projects/XYZeq
In [1204]:
# plot_spatial_array(path+'matrix.mtx', path+'cell_annotate.txt', 
#                    path+'gene_name_annotate.txt', 
#                    path+'plate23_map.csv','mm10_Dpyd', 0)
In [1205]:
# plot_spatial_array(path+'matrix.mtx', path+'cell_annotate_L20C1.txt', 
#                    path+'gene_name_annotate.txt', 
#                    path+'plate23_map.csv','mm10_Ahnak', 0)
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In [ ]:
 
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In [1285]:
ls -lt ./data/20190917
total 1540664
-rw-r--r--  1 derekbogdanoff  staff  387800582 Oct 16 01:08 matrix.mtx
-rw-r--r--@ 1 derekbogdanoff  staff       8507 Oct 14 12:48 lymphocyte_annotate_L20C1.txt
-rw-r--r--@ 1 derekbogdanoff  staff     940590 Oct  2 17:04 cell_annotate_L20C1.txt
-rw-r--r--@ 1 derekbogdanoff  staff    2315261 Sep 20 13:56 barcodes.tsv
-rw-r--r--  1 derekbogdanoff  staff    2035656 Sep 20 13:56 genes.tsv
-rw-r--r--  1 derekbogdanoff  staff    2777705 Sep 20 13:55 cell_annotate.txt
-rw-r--r--  1 derekbogdanoff  staff    8394019 Sep 20 13:54 gene_name_annotate.txt
-rw-r--r--  1 derekbogdanoff  staff  362600895 Sep 20 13:05 matrix_mergedIntron.mtx
-rw-r--r--@ 1 derekbogdanoff  staff      17111 Sep 19 02:07 plate23_map.csv
In [ ]:
 
In [1400]:
sc.settings.verbosity=3
sc.settings.set_figure_params(dpi=80)
sc.logging.print_versions()
results_file = './write/results.h5ad'

#sc.settings = True

##load the data
adata=sc.read(path + 'matrix_mergedIntron.mtx', cache=False).T




# ##name the genes
adata.var_names = [gene.strip() for gene in open(path + 'genes.tsv', 'r')]


# ##name the cells
adata.obs_names = [barcode.strip() for barcode in open(path + 'barcodes.tsv', 'r')] 
scanpy==1.4.3 anndata==0.6.21 umap==0.3.9 numpy==1.17.2 scipy==1.2.2 pandas==0.23.4 scikit-learn==0.20.0 statsmodels==0.9.0 python-igraph==0.7.1 louvain==0.6.1 
--> This might be very slow. Consider passing `cache=True`, which enables much faster reading from a cache file.
Variable names are not unique. To make them unique, call `.var_names_make_unique`.
In [1401]:
adata.var_names_make_unique()
In [1402]:
adata=adata[adata.obs_names.str.contains('L20C1')]
In [1403]:
adata.obs_names
Out[1403]:
Index(['L20C1_P1A01F.GACCGTCTCATAGCGC', 'L20C1_P1A01F.GCCTCTTAGATAGACA',
       'L20C1_P1A01F.GATCCGTCGTCAGACT', 'L20C1_P1A01F.GGTCACCACTTACAGT',
       'L20C1_P1A01F.TTACCGTTGGCCAATA', 'L20C1_P1A01F.TCAACAGAACCACCGA',
       'L20C1_P1A01F.GCAGTACAGACCAGGC', 'L20C1_P1A01F.TAGCCACAAGCGGACA',
       'L20C1_P1A01F.GTCCATGATTAGTAGC', 'L20C1_P1A01F.CTCCTGTCCAAGACAT',
       ...
       'L20C1_P4H12F.TCCACCGTATGCGCTG', 'L20C1_P4H12F.ATCTCTTGTCTTGGCG',
       'L20C1_P4H12F.CTCTTGGAGGTCTTCG', 'L20C1_P4H12F.TACCACGCCTACCAGC',
       'L20C1_P4H12F.CGAGGCTATGCGTTGG', 'L20C1_P4H12F.CTGGTAATAAGAGCGC',
       'L20C1_P4H12F.TACGTGCTAACTGCTA', 'L20C1_P4H12F.TCCGAACCGAGATTAA',
       'L20C1_P4H12F.CATGCGTGCTCTTACG', 'L20C1_P4H12F.ATCGAATGCGCCACCG'],
      dtype='object', length=26436)
In [1404]:
sc.pl.highest_expr_genes(adata, n_top=5) 
In [1405]:
adata
Out[1405]:
View of AnnData object with n_obs × n_vars = 26436 × 121472 
In [ ]:
 
In [1406]:
##post processing filter minimum UMIS and genes
sc.pp.filter_cells(adata, min_counts=500)#300
sc.pp.filter_cells(adata, min_genes=100)

#sc.pp.filter_cells(adata, max_counts=15000)
#sc.pp.filter_cells(adata, max_genes=2000) 

##post processing filter genes 
sc.pp.filter_genes(adata, min_cells=2)
#sc.pp.filter_genes(adata, max_cells=1000)   

## define mito genes 
mito_genes=[name for name in adata.var_names if name.startswith(('mm10_mt-', 'hg19_mt-', 'mt-'))]


##create new obs anotation to define percent mito for each cell 
adata.obs['percent_mito'] = np.sum(adata[:, mito_genes].X, axis=1) / np.sum(adata.X, axis=1)*100

##create new obs anotation to define UMIs per cell
adata.obs['n_counts'] = adata.X.sum(axis=1)
Trying to set attribute `.obs` of view, making a copy.
filtered out 18293 cells that have less than 500 counts
filtered out 3 cells that have less than 100 genes expressed
filtered out 71226 genes that are detected in less than 2 cells
In [ ]:
 
In [1407]:
sc.pl.violin(adata, ['n_counts', 'n_genes', 'percent_mito'], jitter=0.4, multi_panel=True)
In [1408]:
spec_filter= [name for name in adata.var_names if not name.startswith('mm10_Itgam')]
adata=adata[:, spec_filter]
In [1409]:
spec_filter= [name for name in adata.var_names if not name.startswith('mm10_Smim20')]
adata=adata[:, spec_filter]
In [1410]:
spec_filter= [name for name in adata.var_names if not name.startswith('hg19_RP11-323I15.5')]
adata=adata[:, spec_filter]
In [ ]:
 
In [1411]:
##filter out outliers

adata = adata[adata.obs['n_genes'] < 15000, :]
adata = adata[adata.obs['n_counts'] < 10000, :]
adata = adata[adata.obs['percent_mito'] < 1, :]
In [1412]:
sc.pl.scatter(adata, x='n_counts', y='percent_mito')
sc.pl.scatter(adata, x='n_counts', y='n_genes')
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/scanpy/plotting/_anndata.py:311: DeprecationWarning: Use obs_vector instead of _get_obs_array, _get_obs_array will be removed in the future.
  x_arr = adata._get_obs_array(x, use_raw=use_raw, layer=layers[0])
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/anndata/base.py:1618: FutureWarning: In a future version of AnnData, access to `.X` by passing `layer='X'` will be removed. Instead pass `layer=None`.
  FutureWarning
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/scanpy/plotting/_anndata.py:312: DeprecationWarning: Use obs_vector instead of _get_obs_array, _get_obs_array will be removed in the future.
  y_arr = adata._get_obs_array(y, use_raw=use_raw, layer=layers[1])
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/anndata/base.py:1618: FutureWarning: In a future version of AnnData, access to `.X` by passing `layer='X'` will be removed. Instead pass `layer=None`.
  FutureWarning
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/scanpy/plotting/_anndata.py:311: DeprecationWarning: Use obs_vector instead of _get_obs_array, _get_obs_array will be removed in the future.
  x_arr = adata._get_obs_array(x, use_raw=use_raw, layer=layers[0])
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/anndata/base.py:1618: FutureWarning: In a future version of AnnData, access to `.X` by passing `layer='X'` will be removed. Instead pass `layer=None`.
  FutureWarning
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/scanpy/plotting/_anndata.py:312: DeprecationWarning: Use obs_vector instead of _get_obs_array, _get_obs_array will be removed in the future.
  y_arr = adata._get_obs_array(y, use_raw=use_raw, layer=layers[1])
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/anndata/base.py:1618: FutureWarning: In a future version of AnnData, access to `.X` by passing `layer='X'` will be removed. Instead pass `layer=None`.
  FutureWarning
In [ ]:
 
In [ ]:
 
In [1413]:
adata
Out[1413]:
AnnData object with n_obs × n_vars = 4788 × 50241 
    obs: 'n_counts', 'n_genes', 'percent_mito'
    var: 'n_cells'
In [1414]:
#barnyard


mouse_genes=[name for name in adata.var_names if name.startswith('mm10_')]
human_genes=[name for name in adata.var_names if name.startswith('hg19_')]



adata.obs['mouse_counts'] = np.sum(adata[:, mouse_genes].X, axis=1)
adata.obs['human_counts'] = np.sum(adata[:, human_genes].X, axis=1)
In [1415]:
# # fig, ax = plt.subplots(figsize=(6, 1))
# fig.subplots_adjust(bottom=0.5)

cmap = mpl.colors.ListedColormap(['royalblue','lightgrey', 'orangered'])
In [1416]:
np.seterr(divide = 'ignore') 
adata.obs['ratio'] = np.log10(np.true_divide(adata.obs['mouse_counts'], adata.obs['human_counts']))


def cell_call(counts):
        if counts['ratio'] > 0.66:
                return 'M'
        elif counts['ratio'] < -0.66:
                return 'H'
        else:
                return 'C'

            
            
            
adata.obs['cell_call']=adata.obs.apply(lambda counts: cell_call(counts), axis=1)
In [1417]:
plt.rcParams['figure.figsize'] = [20, 20]

plt.scatter(x=adata.obs['mouse_counts'], 
            y=adata.obs['human_counts'],
            s=10,
            c=adata.obs['ratio'],
           cmap=cmap)


plt.xlabel('Mouse Transcripts')
plt.ylabel('Human Transcripts')
plt.grid(b=False)

#plt.savefig('/Users/derekbogdanoff/Desktop/test.svg', bbox_inches='tight')
In [1418]:
sns.violinplot(data=adata.obs,
               x='cell_call',
               y='n_counts',
 #              palette=['lightgrey','royalblue', 'orangered']
              )
Out[1418]:
<matplotlib.axes._subplots.AxesSubplot at 0x14e50aac8>
In [1419]:
sns.violinplot(data=adata.obs,x='cell_call',y='n_genes')
Out[1419]:
<matplotlib.axes._subplots.AxesSubplot at 0x1470c76d8>
In [ ]:

In [1420]:
adata.obs['cell_call'].value_counts()
Out[1420]:
M    2700
H    1625
C     463
Name: cell_call, dtype: int64
In [1421]:
np.median(adata[adata.obs['cell_call']=='M'].obs.n_genes)
Out[1421]:
456.0
In [1473]:
sns.violinplot(data=adata.obs,x='cell_call',y='percent_mito')
Out[1473]:
<matplotlib.axes._subplots.AxesSubplot at 0x14fa2ed30>
In [ ]:
 
In [1422]:
##create raw data file

adata.raw = sc.pp.log1p(adata, copy=True)
In [1423]:
##normalize counts per cell to 10K each 

sc.pp.normalize_per_cell(adata, counts_per_cell_after=1e4)
In [1424]:
print(adata.X.sum(axis=1))
[[10000.   ]
 [ 9999.999]
 [ 9999.999]
 ...
 [10000.   ]
 [10000.   ]
 [10000.002]]
In [1425]:
adata.shape
Out[1425]:
(4788, 50241)
In [1426]:
filter_result = sc.pp.filter_genes_dispersion(
    adata.X, min_mean=0.35, max_mean=7, min_disp=1) ####L20C1 parameters 

    
    
sc.pl.filter_genes_dispersion(filter_result)
In [1427]:
filter_result
Out[1427]:
rec.array([(False, 1.1702676e-03, 0.3601469, -2.6226435 ),
           (False, 1.0000000e-12,       nan,         nan),
           (False, 1.1697747e-02, 2.2933307, -0.49871537), ...,
           (False, 1.4190648e+00, 3.334681 , -1.4423524 ),
           (False, 2.6195091e-01, 3.5203967, -0.20583872),
           (False, 3.8959142e-03, 2.3415518, -0.44573635)],
          dtype=[('gene_subset', '?'), ('means', '<f4'), ('dispersions', '<f4'), ('dispersions_norm', '<f4')])
In [ ]:
 
In [1428]:
sum(filter_result.gene_subset)
Out[1428]:
789
In [1429]:
#percent of genes that are highly variable
sum(filter_result.gene_subset)/len(filter_result.gene_subset)*100
Out[1429]:
1.570430524870126
In [1430]:
adata = adata[:, filter_result.gene_subset]
In [1431]:
adata
Out[1431]:
View of AnnData object with n_obs × n_vars = 4788 × 789 
    obs: 'n_counts', 'n_genes', 'percent_mito', 'mouse_counts', 'human_counts', 'ratio', 'cell_call'
    var: 'n_cells'
In [1432]:
sc.pp.log1p(adata)
In [1433]:
adata.X
Out[1433]:
<4788x789 sparse matrix of type '<class 'numpy.float32'>'
	with 172497 stored elements in Compressed Sparse Row format>
In [1434]:
#Regress out unwanted sources of variation
sc.pp.regress_out(adata, ['n_counts', 'percent_mito'])
... storing 'cell_call' as categorical
regressing out ['n_counts', 'percent_mito']
    sparse input is densified and may lead to high memory use
    finished (0:00:03.21)
In [1435]:
sc.pp.scale(adata, max_value=10)
In [ ]:
 
In [1436]:
sc.tl.pca(adata, svd_solver='arpack')
computing PCA with n_comps = 50
    finished (0:00:00.46)
In [1437]:
sc.pl.pca(adata,color='mm10_Cd74')
In [1438]:
sc.pl.pca_variance_ratio(adata, log=True)
In [1439]:
sc.pl.pca_loadings(adata)
In [1440]:
sc.pp.neighbors(adata, n_neighbors=12, n_pcs=10)
computing neighbors
    using 'X_pca' with n_pcs = 10
    finished (0:00:00.53) --> added to `.uns['neighbors']`
    'distances', distances for each pair of neighbors
    'connectivities', weighted adjacency matrix
In [1441]:
sc.tl.leiden(adata, resolution=0.6)
running Leiden clustering
    finished (0:00:00.63) --> found 11 clusters and added
    'leiden', the cluster labels (adata.obs, categorical)
In [1442]:
sc.tl.louvain(adata, resolution=0.8)
running Louvain clustering
    using the "louvain" package of Traag (2017)
    finished (0:00:00.37) --> found 13 clusters and added
    'louvain', the cluster labels (adata.obs, categorical)
In [1443]:
sc.tl.umap(adata,
            min_dist=.1,
            spread=2,
            alpha=2,
            )
computing UMAP
    using 'X_pca' with n_pcs = 10
    finished (0:00:05.89) --> added
    'X_umap', UMAP coordinates (adata.obsm)
In [ ]:
 
In [ ]:
 
In [1444]:
plt.rcParams['figure.figsize'] = [20, 16]

sc.pl.umap(adata, 
           color=['cell_call','percent_mito','leiden'],
           ncols=1,
           palette="Set3",
           color_map=mpl.cm.CMRmap,
      #     legend_loc='on data',
           size=200)
In [ ]:
 
In [1445]:
sc.tl.rank_genes_groups(adata, 'leiden')
sc.pl.rank_genes_groups(adata, 
                        n_genes=50, 
                 #       groups=[str(3)],
                        ncols=1,
                        fontsize=20,
                        sharey=False
                       )
ranking genes
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:877: RuntimeWarning: invalid value encountered in greater
  return (self.a < x) & (x < self.b)
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:877: RuntimeWarning: invalid value encountered in less
  return (self.a < x) & (x < self.b)
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:1831: RuntimeWarning: invalid value encountered in less_equal
  cond2 = cond0 & (x <= self.a)
    finished (0:00:02.01) --> added to `.uns['rank_genes_groups']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids
In [ ]:
 
In [1446]:
### define new annotation for celltype based on leiden clusters ####

cell_type_df=pd.DataFrame(adata.obs['leiden'])


def label (row):
   if row['leiden'] == '0':
      return 'mc38'
   if row['leiden'] =='1':
      return 'Hepatocyte'
   if row['leiden'] == '2':
      return 'Hek293t'
   if row['leiden'] == '3':
      return 'Hepatocyte'
   if row['leiden']  == '4':
      return 'Hek293t'
   if row['leiden'] == '5':
      return 'Macrophage'
   if row['leiden'] == '6':
      return 'Hek293t'
   if row['leiden'] == '7':
      return 'Hek293t'
   if row['leiden'] == '8':
      return 'Lymphocyte'
   if row['leiden'] == '9':
      return 'LSEC'
   if row['leiden'] == '10':    
      return 'Kupffer'
    
    
cell_type_df_ =  pd.DataFrame(cell_type_df.apply (lambda row: label(row), axis=1),)

cell_type_df_.columns=['CellType']

adata.obs['CellType']=cell_type_df_
In [1470]:
plt.rcParams['figure.figsize'] = [20, 16]

sc.pl.umap(adata, 
           color=['CellType','cell_call'],
           ncols=1,
           palette="Set2",
           color_map=mpl.cm.CMRmap,
           frameon=False,
        #   legend_loc='on data',
        # save='test.svg',
           size=200,
          )
In [1448]:
adata.uns['CellType_colors']
Out[1448]:
['#1f77b4', '#ff7f0e', '#279e68', '#d62728', '#aa40fc', '#8c564b', '#e377c2']
In [1449]:
adata.uns['CellType_colors']=['#e41a1c',
                              '#377eb8',
                              '#984ea3',
                              '#ff7f00',
                              '#ffff33',
                              '#f781bf',
                              '#4daf4a']
In [1450]:
from pylab import *

cmap = cm.get_cmap('Set1')    # PiYG

for i in range(cmap.N):
    rgb = cmap(i)[:3] # will return rgba, we take only first 3 so we get rgb
    print(matplotlib.colors.rgb2hex(rgb))
#e41a1c
#377eb8
#4daf4a
#984ea3
#ff7f00
#ffff33
#a65628
#f781bf
#999999
In [1451]:
adata
Out[1451]:
AnnData object with n_obs × n_vars = 4788 × 789 
    obs: 'n_counts', 'n_genes', 'percent_mito', 'mouse_counts', 'human_counts', 'ratio', 'cell_call', 'leiden', 'louvain', 'CellType'
    var: 'n_cells'
    uns: 'pca', 'neighbors', 'leiden', 'louvain', 'cell_call_colors', 'leiden_colors', 'rank_genes_groups', 'CellType_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
In [1452]:
adata.obs['barcode']=df['barcode']
In [1341]:
adata
Out[1341]:
AnnData object with n_obs × n_vars = 4788 × 789 
    obs: 'n_counts', 'n_genes', 'percent_mito', 'mouse_counts', 'human_counts', 'ratio', 'cell_call', 'leiden', 'louvain', 'CellType', 'barcode'
    var: 'n_cells'
    uns: 'pca', 'neighbors', 'leiden', 'louvain', 'cell_call_colors', 'leiden_colors', 'rank_genes_groups', 'CellType_colors'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs'
In [1458]:
adata.uns["rank_genes_groups"]
Out[1458]:
{'params': {'groupby': 'leiden',
  'reference': 'rest',
  'method': 't-test_overestim_var',
  'use_raw': True,
  'corr_method': 'benjamini-hochberg'},
 'scores': rec.array([(30.928335 , 17.94088  , 11.151877 , 39.470398 , 28.48433  , 18.638718 , 24.144304 , 19.205795 , 8.514408 , 9.394726 , 15.460407 ),
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            (28.117542 , 15.28949  ,  7.2842155, 33.22678  , 25.777569 , 15.769059 , 20.41395  , 15.00026  , 8.227359 , 8.61482  , 12.179902 ),
            (25.381634 , 14.092407 ,  6.8488436, 32.111732 , 24.211544 , 15.148917 , 19.700083 , 14.189016 , 7.141742 , 8.209577 , 10.41317  ),
            (24.53655  , 13.672046 ,  5.582834 , 30.46889  , 22.97294  , 14.893652 , 19.618599 , 14.131254 , 6.9222484, 8.180607 , 10.292681 ),
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            (19.748465 ,  8.293101 ,  3.4073856, 20.567114 , 10.56098  , 10.591693 , 16.963223 , 10.540174 , 4.2482457, 5.8499427,  7.1033535),
            (19.401222 ,  8.291421 ,  3.4023402, 19.649202 , 10.494594 , 10.473043 , 16.84062  , 10.371307 , 4.218741 , 5.838185 ,  7.0889273),
            (19.395784 ,  8.2673   ,  3.387107 , 19.510296 , 10.233861 , 10.169714 , 16.735775 , 10.330887 , 4.1891923, 5.7312455,  6.9724655),
            (19.272697 ,  8.163149 ,  3.3826735, 19.50282  , 10.0269   , 10.159058 , 16.553652 , 10.329525 , 4.184965 , 5.7243886,  6.9305015),
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            (18.982351 ,  7.898977 ,  3.3246021, 19.081627 ,  9.257336 ,  9.885218 , 16.4656   , 10.25497  , 4.0217543, 5.6000543,  6.8322077),
            (18.855309 ,  7.8805137,  3.2863464, 19.053234 ,  9.235137 ,  9.880761 , 16.28018  , 10.195289 , 4.009851 , 5.504044 ,  6.7540236),
            (18.829336 ,  7.7743526,  3.2744272, 18.85113  ,  9.04926  ,  9.705251 , 16.236605 , 10.025089 , 4.0030885, 5.487377 ,  6.711741 ),
            (18.67987  ,  7.736595 ,  3.263241 , 18.724487 ,  9.035882 ,  9.682631 , 16.197962 , 10.018197 , 3.994374 , 5.3745174,  6.626869 ),
            (18.643902 ,  7.6733894,  3.251254 , 18.5771   ,  8.953925 ,  9.582216 , 16.157162 ,  9.983684 , 3.977536 , 5.3726287,  6.302531 ),
            (18.620909 ,  7.6417313,  3.1974778, 18.249763 ,  8.786019 ,  9.576028 , 15.861039 ,  9.958639 , 3.9484675, 5.3565693,  6.2699194),
            (18.506058 ,  7.542913 ,  3.1648967, 18.149199 ,  8.782358 ,  9.480327 , 15.8270235,  9.914612 , 3.9271293, 5.2834687,  6.154606 ),
            (18.43402  ,  7.5379157,  3.1621437, 18.047215 ,  8.484077 ,  9.4611635, 15.670787 ,  9.8741865, 3.888952 , 5.228166 ,  6.1537385),
            (18.429321 ,  7.4907913,  3.132191 , 17.810253 ,  8.429212 ,  9.449677 , 15.487347 ,  9.834232 , 3.8394458, 5.1964817,  6.0669475),
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            (18.181307 ,  7.200797 ,  3.0781116, 17.465715 ,  8.269342 ,  9.262729 , 15.239398 ,  9.6387825, 3.808528 , 5.1305666,  5.9198103),
            (18.00913  ,  7.1877055,  3.0774095, 17.444998 ,  8.250906 ,  9.130271 , 15.158121 ,  9.601304 , 3.8026116, 5.026202 ,  5.8150706),
            (17.854906 ,  7.129716 ,  3.0662868, 17.357439 ,  8.119373 ,  9.129884 , 15.109824 ,  9.546717 , 3.8005824, 4.9968386,  5.7580757),
            (17.813469 ,  7.1083274,  3.0372562, 17.352556 ,  7.980997 ,  8.97014  , 15.087161 ,  9.535901 , 3.7827835, 4.9666405,  5.7429905),
            (17.785217 ,  7.095074 ,  3.000446 , 17.057999 ,  7.9110794,  8.955183 , 14.963282 ,  9.419992 , 3.7645776, 4.9138126,  5.7074275),
            (17.744312 ,  6.952988 ,  2.9817255, 17.016682 ,  7.8945007,  8.831544 , 14.913496 ,  9.33384  , 3.7215502, 4.900034 ,  5.662996 ),
            (17.658607 ,  6.933151 ,  2.9631267, 17.008978 ,  7.810207 ,  8.706383 , 14.818797 ,  9.293763 , 3.714074 , 4.890747 ,  5.566    ),
            (17.510757 ,  6.8899198,  2.9422295, 16.961239 ,  7.805888 ,  8.615458 , 14.807208 ,  9.248791 , 3.712119 , 4.857298 ,  5.529445 ),
            (17.49656  ,  6.820441 ,  2.939288 , 16.73436  ,  7.784818 ,  8.59579  , 14.72531  ,  9.239704 , 3.7060568, 4.8511143,  5.5101757),
            (17.275585 ,  6.803135 ,  2.937989 , 16.638157 ,  7.7522774,  8.491488 , 14.689746 ,  9.216889 , 3.7005548, 4.8378143,  5.5044584),
            (17.275272 ,  6.765053 ,  2.9201992, 16.577238 ,  7.737715 ,  8.415377 , 14.68856  ,  9.2052965, 3.6522038, 4.812755 ,  5.318814 ),
            (17.225435 ,  6.486794 ,  2.8947256, 16.57177  ,  7.731592 ,  8.390724 , 14.667687 ,  9.202094 , 3.6282113, 4.7774997,  5.253942 ),
            (17.106852 ,  6.482006 ,  2.8946543, 16.547411 ,  7.717123 ,  8.385787 , 14.623005 ,  9.200035 , 3.579318 , 4.7630205,  5.1887903),
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            (17.06061  ,  6.245994 ,  2.8355803, 16.471186 ,  7.6058583,  8.315592 , 14.501523 ,  9.14826  , 3.5374484, 4.740155 ,  4.9930477),
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            (17.000372 ,  6.165318 ,  2.8119276, 16.432714 ,  7.52767  ,  8.270254 , 14.475127 ,  9.070787 , 3.5093243, 4.689732 ,  4.8730307),
            (16.980736 ,  6.0725846,  2.7921462, 16.401876 ,  7.4939065,  8.267516 , 14.430164 ,  9.065461 , 3.4730911, 4.675883 ,  4.843959 ),
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            (16.568562 ,  5.9687276,  2.7087183, 16.174068 ,  7.297538 ,  8.141637 , 14.17566  ,  8.82472  , 3.357478 , 4.586766 ,  4.707731 ),
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            (16.521107 ,  5.902976 ,  2.6695254, 15.941047 ,  7.260275 ,  8.107976 , 14.125307 ,  8.808302 , 3.3329365, 4.5561123,  4.699374 ),
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            (16.486979 ,  5.853015 ,  2.6636195, 15.739305 ,  7.224628 ,  8.03631  , 14.081908 ,  8.777808 , 3.2709775, 4.5557475,  4.616375 ),
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            (16.420588 ,  5.663792 ,  2.6201727, 15.469606 ,  7.1070266,  7.909335 , 14.05057  ,  8.739738 , 3.2342377, 4.541951 ,  4.5229144),
            (16.361565 ,  5.6252646,  2.6157808, 15.464538 ,  7.101039 ,  7.906077 , 14.047439 ,  8.732975 , 3.2275388, 4.525926 ,  4.519065 ),
            (16.353037 ,  5.585821 ,  2.5852091, 15.341842 ,  7.0580783,  7.842109 , 14.035091 ,  8.714001 , 3.225112 , 4.518324 ,  4.51526  ),
            (16.222029 ,  5.553422 ,  2.581827 , 15.297552 ,  7.040007 ,  7.8405566, 13.985652 ,  8.701598 , 3.2165918, 4.480601 ,  4.470837 ),
            (16.195889 ,  5.5335584,  2.5797772, 15.222629 ,  7.025314 ,  7.7194867, 13.976565 ,  8.660025 , 3.1973717, 4.4611526,  4.4169707),
            (16.124733 ,  5.5169535,  2.5681891, 15.202412 ,  6.9948263,  7.702914 , 13.947534 ,  8.653495 , 3.1925893, 4.461076 ,  4.3653045),
            (16.121574 ,  5.500221 ,  2.5516238, 15.152212 ,  6.9198723,  7.7008133, 13.8695345,  8.65039  , 3.1809003, 4.4591193,  4.3391843),
            (16.081314 ,  5.4944696,  2.547099 , 15.147145 ,  6.91296  ,  7.6677685, 13.860097 ,  8.629585 , 3.1735606, 4.4515057,  4.289768 ),
            (16.055716 ,  5.4555736,  2.5416734, 15.092371 ,  6.9093904,  7.6300035, 13.855664 ,  8.618226 , 3.172935 , 4.438581 ,  4.277117 ),
            (16.030542 ,  5.4355946,  2.5390964, 15.054578 ,  6.862097 ,  7.6137342, 13.827511 ,  8.582009 , 3.1686618, 4.404909 ,  4.2550316),
            (15.949504 ,  5.409764 ,  2.5390964, 15.016123 ,  6.860929 ,  7.6022644, 13.81895  ,  8.5801   , 3.1612697, 4.403454 ,  4.2506447),
            (15.936728 ,  5.407934 ,  2.5025725, 14.965521 ,  6.8118424,  7.5480385, 13.81849  ,  8.57043  , 3.1548414, 4.3693614,  4.175232 ),
            (15.933606 ,  5.4067106,  2.501807 , 14.927112 ,  6.8029823,  7.531331 , 13.767931 ,  8.547019 , 3.149852 , 4.358664 ,  4.1739335),
            (15.9143305,  5.35581  ,  2.4975827, 14.921802 ,  6.7938323,  7.518512 , 13.760027 ,  8.534824 , 3.1280828, 4.347739 ,  4.1728635),
            (15.906981 ,  5.3332686,  2.48921  , 14.920014 ,  6.753207 ,  7.495663 , 13.7595215,  8.471492 , 3.1073751, 4.3357754,  4.167288 ),
            (15.903272 ,  5.305777 ,  2.4866352, 14.75148  ,  6.7469645,  7.4445114, 13.755375 ,  8.464434 , 3.092446 , 4.3355603,  4.1272063),
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            (15.796361 ,  5.2683554,  2.4792223, 14.693488 ,  6.727355 ,  7.2487836, 13.734384 ,  8.44445  , 3.0521348, 4.2432747,  4.047407 ),
            (15.7808075,  5.206965 ,  2.476532 , 14.632867 ,  6.725652 ,  7.230548 , 13.668144 ,  8.42826  , 3.02153  , 4.238151 ,  4.0314784),
            (15.75249  ,  5.204358 ,  2.4586072, 14.492794 ,  6.7252116,  7.2068634, 13.620735 ,  8.427625 , 2.9906092, 4.2374854,  4.0048895),
            (15.747858 ,  5.183746 ,  2.4578614, 14.335227 ,  6.7139277,  7.1866307, 13.582573 ,  8.423433 , 2.9585822, 4.2254367,  3.9540894),
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            (15.568606 ,  5.1467843,  2.449387 , 14.165129 ,  6.6872783,  7.1174197, 13.467799 ,  8.378906 , 2.9481883, 4.1746078,  3.893541 ),
            (15.494284 ,  5.1080217,  2.44756  , 14.114667 ,  6.661425 ,  7.094975 , 13.382619 ,  8.365392 , 2.8904734, 4.158786 ,  3.8737252),
            (15.485809 ,  5.0886393,  2.4334543, 14.0541115,  6.652151 ,  7.070554 , 13.356089 ,  8.358854 , 2.8886635, 4.141141 ,  3.8452103),
            (15.412458 ,  5.069423 ,  2.4319143, 14.006565 ,  6.6048794,  7.062499 , 13.339637 ,  8.348851 , 2.8606787, 4.065028 ,  3.8446577),
            (15.405137 ,  5.05119  ,  2.4316542, 13.963928 ,  6.6003575,  7.055178 , 13.297605 ,  8.340739 , 2.8080094, 4.0488334,  3.8230002),
            (15.39467  ,  5.0339427,  2.4299686, 13.95613  ,  6.5798664,  7.049553 , 13.296237 ,  8.319803 , 2.8055384, 4.015296 ,  3.7831964),
            (15.382987 ,  5.007454 ,  2.422109 , 13.953427 ,  6.543673 ,  7.0266333, 13.288316 ,  8.308306 , 2.8048048, 4.0029163,  3.75543  ),
            (15.381092 ,  4.9977937,  2.4164398, 13.896556 ,  6.534927 ,  7.011061 , 13.232812 ,  8.301843 , 2.7987695, 3.9987988,  3.748172 ),
            (15.357598 ,  4.989409 ,  2.4155838, 13.809582 ,  6.5346394,  6.9948487, 13.188271 ,  8.273083 , 2.7936416, 3.99012  ,  3.7349439),
            (15.35092  ,  4.984553 ,  2.411328 , 13.786014 ,  6.5249453,  6.9864693, 13.163932 ,  8.258467 , 2.7883148, 3.9786162,  3.7161293),
            (15.349244 ,  4.964763 ,  2.410823 , 13.774395 ,  6.5179777,  6.985451 , 13.152818 ,  8.207459 , 2.7824461, 3.9676769,  3.6914725)],
           dtype=[('0', '<f4'), ('1', '<f4'), ('2', '<f4'), ('3', '<f4'), ('4', '<f4'), ('5', '<f4'), ('6', '<f4'), ('7', '<f4'), ('8', '<f4'), ('9', '<f4'), ('10', '<f4')]),
 'names': rec.array([('mm10_Zfpm2', 'mm10_Alb', 'hg19_MALAT1', 'mm10_Dpyd', 'hg19_MT-CO3', 'mm10_Cd74', 'hg19_MALAT1', 'hg19_XIST', 'mm10_Dock10', 'mm10_Chst15', 'mm10_Ptprj'),
            ('mm10_Ahnak', 'mm10_Trf', 'hg19_XIST', 'mm10_Abcc2', 'hg19_MT-ND4', 'mm10_Zeb2', 'hg19_XIST', 'hg19_MALAT1', 'mm10_Skap1', 'mm10_Tshz2', 'mm10_Cd5l'),
            ('mm10_Plec', 'mm10_Pigr', 'hg19_MT-CO3', 'mm10_Ppara', 'hg19_MT-ATP6', 'mm10_Tgfbi', 'hg19_MT-CO3', 'hg19_LINC00669', 'mm10_Inpp4b', 'mm10_Stab2', 'mm10_Fyb'),
            ('mm10_Dpysl3', 'mm10_Ppara', 'hg19_LUC7L3', 'mm10_Ghr', 'hg19_MT-CYB', 'mm10_Ccr5', 'hg19_MT-ND4', 'hg19_WWOX', 'mm10_Ptprc', 'mm10_Prkg1', 'mm10_Dock10'),
            ('mm10_Myh9', 'mm10_Dpyd', 'hg19_MT-ND4', 'mm10_Pzp', 'hg19_MT-ND2', 'mm10_Slc8a1', 'hg19_LUC7L3', 'hg19_FHIT', 'mm10_Arhgap15', 'mm10_Ptprb', 'mm10_Zeb2'),
            ('mm10_Dst', 'mm10_Gc', 'hg19_WWOX', 'mm10_Mug1', 'hg19_MT-ND3', 'mm10_1700112E06Rik', 'hg19_MT-ND2', 'hg19_NEAT1', 'mm10_Prkch', 'mm10_Dlc1', 'mm10_Adap2'),
            ('mm10_Il34', 'mm10_Apoa1', 'hg19_MT-ATP6', 'mm10_Malat1', 'hg19_MT-ATP8', 'mm10_Ctss', 'hg19_MT-CO2', 'hg19_AUTS2', 'mm10_Dock2', 'mm10_Igfbp7', 'mm10_Timd4'),
            ('mm10_Cald1', 'mm10_Pzp', 'hg19_LINC00669', 'mm10_Adk', 'hg19_MT-CO2', 'mm10_Actb', 'hg19_MT-CYB', 'hg19_RAD51B', 'mm10_Elmo1', 'mm10_Gpc6', 'mm10_Mertk'),
            ('mm10_Erdr1', 'mm10_Ahsg', 'hg19_MT-CYB', 'mm10_Apob', 'hg19_MT-CO1', 'mm10_Myo1f', 'hg19_WWOX', 'hg19_SLIT2', 'mm10_Ppp1r16b', 'mm10_Rbms3', 'mm10_Adgb'),
            ('mm10_Hsp90ab1', 'mm10_Mug1', 'hg19_FHIT', 'mm10_Immp2l', 'hg19_MT-ND5', 'mm10_Arhgap15', 'hg19_FAF1', 'hg19_GPC6', 'mm10_Ikzf1', 'mm10_Rora', 'mm10_Marco'),
            ('mm10_Rbfox2', 'mm10_Abcc2', 'hg19_EIF4G3', 'mm10_Gc', 'hg19_MT-ND1', 'mm10_Ly86', 'hg19_RAD51B', 'hg19_TTC28', 'mm10_Hmha1', 'mm10_Adgrl4', 'mm10_Ptprm'),
            ('mm10_Ldlrad4', 'mm10_Apoe', 'hg19_MT-ND2', 'mm10_Alb', 'hg19_MT-ND4L', 'mm10_Dock10', 'hg19_MT-ATP6', 'hg19_BCAS3', 'mm10_Il18r1', 'mm10_1700112E06Rik', 'mm10_Aoah'),
            ('mm10_Gm42418', 'mm10_Adk', 'hg19_MT-ND3', 'mm10_Ahsg', 'hg19_MT-ND6', 'mm10_Gab2', 'hg19_LINC00669', 'hg19_LRP1B', 'mm10_Myo1f', 'mm10_Palld', 'mm10_Pou2f2'),
            ('mm10_Zeb1', 'mm10_Ghr', 'hg19_GPC6', 'mm10_Egfr', 'hg19_MTRNR2L1', 'mm10_Inpp5d', 'hg19_MT-CO1', 'hg19_GNGT1', 'mm10_Prkcq', 'mm10_Ccdc80', 'mm10_Adgre1'),
            ('mm10_Fbn1', 'mm10_Apob', 'hg19_DNAH14', 'mm10_Trf', 'hg19_MTRNR2L12', 'mm10_Fyb', 'hg19_PUM1', 'hg19_DLEU1', 'mm10_Ikzf2', 'mm10_Kalrn', 'mm10_Clec4f'),
            ('mm10_Fam19a1', 'mm10_Apoc1', 'mm10_Zfp804b', 'mm10_Hnf1aos1', 'hg19_LUC7L3', 'mm10_Hck', 'hg19_ANKRD11', 'hg19_ROBO1', 'mm10_Dennd4a', 'mm10_Palm2', 'mm10_Myo9a'),
            ('mm10_Sparc', 'mm10_C3', 'hg19_ZRANB2', 'mm10_Slco1b2', 'hg19_MALAT1', 'mm10_Unc93b1', 'hg19_SLIT2', 'hg19_FAF1', 'mm10_Dock8', 'mm10_Casp4', 'mm10_Adgre4'),
            ('mm10_Ext1', 'mm10_Fgb', 'hg19_RERE', 'mm10_Mug2', 'hg19_MTRNR2L8', 'mm10_Ptprc', 'hg19_FHIT', 'hg19_DOCK3', 'mm10_Fam65b', 'mm10_St3gal4', 'mm10_Slc8a1'),
            ('mm10_Msn', 'mm10_Itih3', 'hg19_RAD51B', 'mm10_Sox5', 'mm10_Zfp804b', 'mm10_Elmo1', 'hg19_MT-ND3', 'hg19_GPHN', 'mm10_Ptpn22', 'mm10_Malat1', 'mm10_Myo1f'),
            ('mm10_Samd4', 'mm10_Mug2', 'hg19_MT-ND1', 'mm10_Cps1', 'hg19_PRRC2C', 'mm10_Tmsb4x', 'hg19_AUTS2', 'hg19_MECOM', 'mm10_Itk', 'mm10_Col4a2', 'mm10_Frmd4b'),
            ('mm10_Hmga2', 'mm10_Cmss1', 'hg19_TRIT1', 'mm10_Akr1c6', 'hg19_HSP90B1', 'mm10_Lyn', 'hg19_MT-ND5', 'hg19_BAI3', 'mm10_Bcl2', 'mm10_Pde3a', 'mm10_Unc93b1'),
            ('mm10_Col3a1', 'mm10_Cps1', 'hg19_PRRC2C', 'mm10_Xist', 'hg19_CANX', 'mm10_Pde7b', 'hg19_BCAS3', 'hg19_PRKG1', 'mm10_Mir142hg', 'mm10_Meis2', 'mm10_Itga9'),
            ('mm10_Actb', 'mm10_Scd1', 'hg19_DGKG', 'mm10_Gphn', 'hg19_RPL5', 'mm10_Cybb', 'hg19_TTC28', 'hg19_JPX', 'mm10_Runx3', 'mm10_C1qtnf7', 'mm10_Dmpk'),
            ('mm10_Apbb2', 'mm10_Itih4', 'hg19_BAI3', 'mm10_Cyp2e1', 'hg19_XIST', 'mm10_Nlrp1b', 'hg19_EIF4G3', 'hg19_TLE4', 'mm10_Themis', 'mm10_Pdzrn3', 'mm10_Gm15675'),
            ('mm10_Lgals1', 'mm10_H2-Q10', 'hg19_TTC28', 'mm10_Plg', 'hg19_RP5-857K21.4', 'mm10_Tgfbr1', 'hg19_SRSF11', 'hg19_KLF12', 'mm10_Ikzf3', 'mm10_Col1a2', 'mm10_Madd'),
            ('mm10_Zfhx3', 'mm10_Abcb11', 'hg19_MTRNR2L8', 'mm10_Zbtb20', 'hg19_RBM25', 'mm10_Fcer1g', 'hg19_NEAT1', 'hg19_NLGN1', 'mm10_Mbp', 'mm10_Ldb2', 'mm10_Ikbke'),
            ('mm10_Gse1', 'mm10_Sorbs2', 'hg19_MT-CO1', 'mm10_Iqgap2', 'hg19_RPL38', 'mm10_Marcks', 'hg19_PRRC2C', 'hg19_TBL1X', 'mm10_Gimap4', 'mm10_Macf1', 'mm10_Hck'),
            ('mm10_Tcf20', 'mm10_Serpina1d', 'hg19_MT-CO2', 'mm10_Erbb4', 'hg19_NCL', 'mm10_Ccr2', 'hg19_RBM39', 'hg19_EXT1', 'mm10_Epsti1', 'mm10_Ror1', 'mm10_Btbd11'),
            ('mm10_Gm26917', 'mm10_Erbb4', 'hg19_EIF3A', 'mm10_Abcb11', 'hg19_RPS6', 'mm10_Malat1', 'hg19_STAG1', 'hg19_ANKRD11', 'mm10_Tmem163', 'mm10_F8', 'mm10_St3gal5'),
            ('mm10_Trio', 'mm10_Sox5', 'hg19_PTPRG', 'mm10_Cfh', 'hg19_RPL36', 'mm10_H2-Ab1', 'hg19_PRKG1', 'hg19_CDC42BPA', 'mm10_Stk39', 'mm10_Adam12', 'mm10_Itsn1'),
            ('mm10_Map1b', 'mm10_Immp2l', 'hg19_ASH1L', 'mm10_Fgb', 'hg19_RPS8', 'mm10_Aoah', 'hg19_MT-ATP8', 'hg19_SMYD3', 'mm10_St6galnac3', 'mm10_Timp3', 'mm10_Macf1'),
            ('mm10_Asap1', 'mm10_Apoc3', 'hg19_AUTS2', 'mm10_Itih3', 'hg19_DGKG', 'mm10_Dock2', 'hg19_ROBO1', 'hg19_DNAH14', 'mm10_Ccdc88c', 'mm10_Pcdh7', 'mm10_Tmcc3'),
            ('mm10_Myo10', 'mm10_Fga', 'hg19_PIBF1', 'mm10_Sult2a8', 'hg19_RPLP1', 'mm10_Fam49b', 'hg19_RBM25', 'hg19_LUC7L3', 'mm10_Fli1', 'mm10_Adam23', 'mm10_Kcna2'),
            ('mm10_Slit2', 'mm10_Kng1', 'hg19_CDC42BPA', 'mm10_Kng1', 'hg19_SRSF11', 'mm10_Ctsc', 'hg19_DDX17', 'hg19_HDAC8', 'mm10_Itpkb', 'mm10_Plekhg1', 'mm10_Zfp710'),
            ('mm10_Ano6', 'mm10_Ido2', 'hg19_CRTC1', 'mm10_Pid1', 'hg19_DHFR', 'mm10_Cx3cr1', 'hg19_HDAC8', 'hg19_FOXP2', 'mm10_Coro2a', 'mm10_Eng', 'mm10_Usp12'),
            ('mm10_Tead1', 'hg19_ALB', 'hg19_CACNA2D3', 'mm10_Dennd5b', 'hg19_RPS21', 'mm10_Gm26740', 'hg19_GPC6', 'hg19_RORA', 'mm10_Itga4', 'mm10_Prex2', 'mm10_St3gal4'),
            ('mm10_Utrn', 'mm10_Plg', 'hg19_SMYD3', 'mm10_Mlxipl', 'hg19_NPM1', 'mm10_C1qa', 'hg19_DOCK3', 'hg19_SRSF11', 'mm10_Scml4', 'mm10_Adamts2', 'mm10_Fcer1g'),
            ('mm10_Top2a', 'mm10_Aldob', 'hg19_PRKG1', 'mm10_9030622O22Rik', 'hg19_RPS4X', 'mm10_Abr', 'hg19_GMDS', 'hg19_ATXN1', 'mm10_Foxn3', 'mm10_Cyyr1', 'mm10_Lcp2'),
            ('mm10_Atrx', 'mm10_Cp', 'hg19_MTRNR2L12', 'mm10_Mtss1', 'hg19_HNRNPU', 'mm10_Apobec1', 'hg19_MT-ND1', 'hg19_PTPRG', 'mm10_Aff3', 'mm10_Col4a1', 'mm10_Slc40a1'),
            ('mm10_Vcan', 'mm10_Hnf1aos1', 'hg19_PCDH9', 'mm10_Igf1', 'hg19_EIF3A', 'mm10_Nrros', 'hg19_CSNK1E', 'hg19_UBE3C', 'mm10_Lck', 'mm10_Sparc', 'mm10_1700112E06Rik'),
            ('mm10_Fstl1', 'mm10_Sema5a', 'hg19_PCSK6', 'mm10_Mtus1', 'hg19_GNB2L1', 'mm10_Ptprj', 'hg19_SFPQ', 'hg19_KMT2C', 'mm10_Lsp1', 'mm10_Il6st', 'mm10_Lyn'),
            ('mm10_Dclk1', 'mm10_Hpx', 'hg19_REV3L', 'mm10_Kynu', 'hg19_RPL39', 'mm10_Ciita', 'hg19_CUX1', 'hg19_CUX1', 'mm10_Ankrd44', 'mm10_Zeb2', 'mm10_C6'),
            ('mm10_Lrrk1', 'mm10_Hacl1', 'hg19_MEGF11', 'mm10_Prr16', 'hg19_ANKRD11', 'mm10_Zfp710', 'hg19_JPX', 'hg19_BBX', 'mm10_Pik3cd', 'mm10_Sema5a', 'mm10_Dse'),
            ('mm10_Pvt1', 'mm10_Mat1a', 'hg19_GNGT1', 'mm10_Aox3', 'mm10_Zfp558', 'mm10_AU020206', 'hg19_ATRX', 'hg19_NFIA', 'mm10_Camk4', 'mm10_4930448N21Rik', 'mm10_Abcg3'),
            ('mm10_Rnf213', 'mm10_Pck1', 'hg19_SNORD3A', 'mm10_Slc7a2', 'hg19_APLP2', 'mm10_Cyth4', 'hg19_SNRNP70', 'hg19_EXOC4', 'mm10_Tmsb4x', 'mm10_Pde7b', 'mm10_Dlc1'),
            ('mm10_Fbxl7', 'mm10_Aox3', 'hg19_CSPP1', 'mm10_Ido2', 'hg19_RPL27A', 'mm10_Mndal', 'hg19_TBL1X', 'hg19_PLCB1', 'mm10_Samsn1', 'mm10_Dse', 'mm10_Prkcb'),
            ('mm10_Col6a3', 'mm10_Apoa2', 'hg19_GMDS', 'mm10_Fam214a', 'hg19_RPL34', 'mm10_Spon1', 'hg19_NUCKS1', 'hg19_GMDS', 'mm10_Gm4955', 'mm10_Hecw2', 'mm10_Mndal'),
            ('mm10_Rai14', 'mm10_Serpina1b', 'hg19_NKTR', 'mm10_Hacl1', 'hg19_RPS3', 'mm10_Dock4', 'hg19_N4BP2L2', 'hg19_PIBF1', 'mm10_Akap13', 'mm10_Inpp5a', 'mm10_Trpm2'),
            ('mm10_Trps1', 'mm10_Mlxipl', 'hg19_HS3ST2', 'mm10_Neat1', 'hg19_HSP90AA1', 'mm10_Mgat5', 'hg19_KLF12', 'hg19_NRXN3', 'mm10_Bcl11b', 'mm10_Fgd5', 'mm10_Slc9a9'),
            ('mm10_Nav1', 'mm10_Slc38a3', 'hg19_TBL1X', 'mm10_Hmgcs2', 'hg19_RP11-60A8.1', 'mm10_Rnf213', 'hg19_DNAH14', 'hg19_PRR4', 'mm10_Cd247', 'mm10_Lhfp', 'mm10_March1'),
            ('mm10_AU020206', 'mm10_Igf1', 'hg19_THSD4', 'mm10_Apoa1', 'hg19_RPLP2', 'mm10_Epsti1', 'hg19_ASH1L', 'hg19_LRBA', 'mm10_Satb1', 'mm10_Syne1', 'mm10_Gm26740'),
            ('mm10_Apobec3', 'mm10_Cfh', 'hg19_SRSF11', 'mm10_C3', 'hg19_RPL18', 'mm10_Eef1a1', 'hg19_PIBF1', 'hg19_FOXP1', 'mm10_Tox', 'mm10_Col5a2', 'mm10_Bank1'),
            ('mm10_Luc7l3', 'mm10_Prr16', 'hg19_ANKRD11', 'mm10_Cped1', 'hg19_RPL13', 'mm10_Lgmn', 'hg19_HELLS', 'hg19_NR6A1', 'mm10_Fmnl1', 'mm10_Bicc1', 'mm10_Nrros'),
            ('mm10_Rpl8', 'mm10_Aldh1l1', 'hg19_NFIA', 'mm10_Sorbs2', 'hg19_NUCKS1', 'mm10_Parp14', 'hg19_LRP1B', 'hg19_CDKAL1', 'mm10_Ccnd3', 'mm10_Plcl1', 'mm10_Mrc1'),
            ('mm10_Cacna2d1', 'mm10_Fabp1', 'hg19_SRRM1', 'mm10_Hc', 'hg19_DEK', 'mm10_Dock8', 'hg19_BPTF', 'hg19_MTHFD1L', 'mm10_Ccr5', 'mm10_Prkch', 'mm10_Slc8b1'),
            ('mm10_Actn1', 'mm10_C4b', 'hg19_BCAS3', 'mm10_Agmo', 'hg19_RPL27', 'mm10_Rreb1', 'hg19_PNISR', 'hg19_BRE', 'mm10_Stat4', 'mm10_Pde1a', 'mm10_Rasgef1b'),
            ('mm10_Malat1', 'mm10_Cyp2e1', 'hg19_CREB5', 'mm10_Cyp3a25', 'hg19_BPTF', 'mm10_Pip4k2a', 'hg19_GPHN', 'hg19_ZNF638', 'mm10_Ms4a4b', 'mm10_Pard3b', 'mm10_Wdfy3'),
            ('mm10_Pakap', 'mm10_Hpd', 'hg19_RORA', 'mm10_Cyp2b9', 'hg19_DDX17', 'mm10_Mef2a', 'hg19_TAF15', 'hg19_KDM6A', 'mm10_Cd2', 'mm10_Prkce', 'mm10_Ptprc'),
            ('mm10_Gm38399', 'mm10_Serpina1a', 'hg19_PABPC1', 'mm10_Slc38a3', 'hg19_RPS24', 'mm10_Ctsb', 'hg19_SMYD3', 'hg19_RFC3', 'mm10_Pfkfb3', 'mm10_Prickle1', 'mm10_Maf'),
            ('mm10_Cdk14', 'mm10_Ttr', 'hg19_DOCK3', 'mm10_Uox', 'hg19_RBM39', 'mm10_Stat1', 'hg19_YWHAE', 'hg19_CREB5', 'mm10_Cables1', 'mm10_Itga9', 'mm10_Ms4a8a'),
            ('mm10_Runx1', 'mm10_Snx24', 'hg19_NEIL3', 'mm10_Fga', 'hg19_RPL3', 'mm10_Tbxas1', 'hg19_ATXN1', 'hg19_FARS2', 'mm10_Gimap3', 'mm10_Sntg1', 'mm10_Pigr'),
            ('mm10_Prkca', 'hg19_ANKRD30BL', 'hg19_VPS13D', 'mm10_Rapgef4', 'hg19_CENPF', 'mm10_Ms4a6c', 'hg19_PNN', 'hg19_UBE2E2', 'mm10_Mapkapk3', 'mm10_Phldb2', 'mm10_Zdhhc14'),
            ('mm10_Pmepa1', 'mm10_Slco1b2', 'hg19_AGAP1', 'mm10_Hpd', 'hg19_NOP56', 'mm10_Hsp90ab1', 'hg19_FOXP1', 'hg19_SDK1', 'mm10_Mbnl1', 'mm10_Pakap', 'mm10_Sdc3'),
            ('mm10_Tenm3', 'mm10_Fgg', 'hg19_SREK1', 'mm10_Prlr', 'hg19_EIF4A1', 'mm10_Klra2', 'hg19_HSP90B1', 'hg19_AGAP1', 'mm10_Thy1', 'mm10_Plxna4', 'mm10_Csf1r'),
            ('mm10_Bnc2', 'mm10_Acox1', 'hg19_ALG13', 'mm10_Thrb', 'hg19_PRKDC', 'mm10_Trim30a', 'hg19_HNRNPU', 'hg19_EPHA6', 'mm10_A430078G23Rik', 'mm10_Nrxn3', 'mm10_Pitpnc1'),
            ('mm10_Vim', 'mm10_Slc27a2', 'hg19_MKLN1', 'mm10_Ang', 'hg19_ATRX', 'mm10_Pik3ap1', 'hg19_SMARCC1', 'hg19_STAG1', 'mm10_Sytl2', 'mm10_Itsn1', 'mm10_Cfp'),
            ('mm10_Wnk1', 'mm10_Serpina1e', 'hg19_AKT3', 'mm10_Ces3a', 'hg19_SMARCC1', 'mm10_Etv6', 'hg19_SRSF4', 'hg19_NSMCE2', 'mm10_Card11', 'mm10_Rarb', 'mm10_Sirpa'),
            ('mm10_Rock2', 'mm10_Vtn', 'hg19_CHAF1A', 'mm10_Naaladl2', 'hg19_RPL23A', 'mm10_March1', 'hg19_ANKRD17', 'hg19_TNRC6B', 'mm10_Klrk1', 'mm10_Plxnc1', 'mm10_Fam129a'),
            ('mm10_Nrp2', 'mm10_Malat1', 'hg19_FAF1', 'mm10_Gfra1', 'hg19_GAPDH', 'mm10_Samhd1', 'hg19_MECOM', 'hg19_THOC2', 'mm10_Peli1', 'mm10_Hmcn1', 'mm10_Ctsb'),
            ('mm10_Prrc2c', 'mm10_Hnf4a', 'hg19_PLD5', 'mm10_Cmah', 'hg19_PCBD2', 'mm10_Adap2', 'hg19_NFIA', 'hg19_EIF4G3', 'mm10_Coro1a', 'mm10_Cacnb2', 'mm10_Spi1'),
            ('mm10_Tpm1', 'mm10_Itih2', 'hg19_NLGN1', 'mm10_Cp', 'hg19_NOP58', 'mm10_Laptm5', 'hg19_PRR4', 'hg19_ANKRD17', 'mm10_Mndal', 'mm10_Tmtc1', 'mm10_Fgr'),
            ('mm10_Eif3a', 'mm10_Sult2a8', 'hg19_PNISR', 'mm10_H2-Q10', 'hg19_MTRNR2L3', 'mm10_C1qb', 'hg19_RBM6', 'hg19_RBM26', 'mm10_Nedd9', 'mm10_Il1r1', 'mm10_Nr1h3'),
            ('mm10_Ccnd1', 'mm10_Hp', 'hg19_EXT1', 'mm10_Hgd', 'hg19_SFPQ', 'mm10_Ikzf1', 'hg19_AKT3', 'hg19_ANK2', 'mm10_Gm44174', 'mm10_Adamts9', 'mm10_Ly86'),
            ('mm10_Rps8', 'mm10_Apoa5', 'hg19_RABGAP1L', 'mm10_St3gal4', 'hg19_RPS2', 'mm10_Maml3', 'hg19_MSI2', 'hg19_RBM39', 'mm10_Atxn1', 'mm10_Ank3', 'mm10_Sema5a'),
            ('mm10_Rpsa', 'mm10_Egfr', 'hg19_PLS3', 'mm10_Ptprd', 'hg19_RPS19', 'mm10_Ppm1h', 'hg19_KMT2C', 'hg19_ASH1L', 'mm10_Ubash3b', 'mm10_Itga1', 'mm10_Gm5150'),
            ('mm10_Ly6e', 'mm10_Mtss1', 'hg19_MDGA2-1', 'mm10_Ass1', 'hg19_RPL31', 'mm10_Spi1', 'hg19_SRRM2', 'hg19_ARL15', 'mm10_Ppp3cc', 'mm10_Col14a1', 'mm10_Pik3ap1'),
            ('mm10_Lpp', 'mm10_9030622O22Rik', 'hg19_MDGA2', 'mm10_Ttr', 'hg19_RPL28', 'mm10_Arhgap30', 'hg19_CDC42BPA', 'hg19_MSI2', 'mm10_Icos', 'mm10_Rbms1', 'mm10_Alb'),
            ('mm10_Baz1b', 'mm10_Hamp', 'hg19_LRP1B', 'mm10_C4b', 'hg19_PRRC2B', 'mm10_H2-Aa', 'hg19_KANSL1', 'hg19_ARID1B', 'mm10_Cers4', 'mm10_Epha3', 'mm10_Adam33'),
            ('mm10_Mtdh', 'mm10_Chsy3', 'hg19_SIRT1', 'mm10_Vwa8', 'hg19_KMT2A', 'mm10_Cd84', 'hg19_CMSS1', 'hg19_BPTF', 'mm10_Cyfip2', 'mm10_Zfpm2', 'mm10_Actb'),
            ('mm10_Inhba', 'mm10_Serpina3k', 'hg19_NSMCE2', 'mm10_Apoe', 'hg19_RPS14', 'mm10_Uvrag', 'hg19_NKTR', 'hg19_PUM1', 'mm10_Gng2', 'mm10_Kif26b', 'mm10_Csf3r'),
            ('mm10_Cbx5', 'mm10_Serpina1c', 'hg19_LL0XNC01-237H1.2', 'mm10_Slc27a2', 'hg19_HNRNPA1', 'mm10_Myh9', 'hg19_MTHFD1L', 'hg19_BIRC6', 'mm10_Itgal', 'mm10_Sulf1', 'mm10_Vsig4'),
            ('mm10_Ddr2', 'mm10_Cmah', 'hg19_PLEKHA5', 'mm10_Tox', 'hg19_RPL11', 'mm10_Cers6', 'hg19_PRKDC', 'hg19_FTX', 'hg19_COBL', 'mm10_Cald1', 'mm10_AF251705'),
            ('mm10_Sptbn1', 'mm10_Uox', 'hg19_MT-ND5', 'mm10_Fabp1', 'hg19_RPL8', 'mm10_Frmd4b', 'hg19_RABGAP1L', 'hg19_KANSL1', 'mm10_Cblb', 'mm10_Plcb4', 'mm10_Rgl1'),
            ('mm10_Naa15', 'mm10_Hsd17b2', 'hg19_SENP6', 'mm10_Aldob', 'hg19_ASH1L', 'mm10_Tmcc3', 'hg19_LRBA', 'hg19_MAGI1', 'mm10_Celf2', 'mm10_Nkain2', 'mm10_P2rx4'),
            ('mm10_Cd44', 'mm10_St3gal4', 'hg19_CEP85', 'mm10_Nr1h4', 'hg19_PUM1', 'mm10_Ubash3b', 'hg19_FUS', 'hg19_CENPP', 'mm10_Ankrd12', 'mm10_Hivep3', 'mm10_Msr1'),
            ('mm10_Ccdc88a', 'mm10_Cped1', 'hg19_MID1', 'mm10_A1cf', 'hg19_RPL37A', 'mm10_Cst3', 'hg19_PRRC2B', 'hg19_ZBTB20', 'mm10_Ptk2b', 'mm10_Raph1', 'mm10_Arhgap15'),
            ('mm10_Gm20388', 'mm10_Shroom3', 'hg19_LINC00893', 'mm10_Mat1a', 'hg19_ANP32B', 'mm10_Entpd1', 'hg19_GOLGA4', 'hg19_AKT3', 'mm10_Def6', 'mm10_Cacna1c', 'mm10_Ppp1r9a'),
            ('mm10_Sh3d19', 'mm10_Scp2', 'hg19_RNF38', 'mm10_Park2', 'hg19_SNRNP70', 'mm10_AI607873', 'hg19_GNGT1', 'hg19_TSHZ2', 'mm10_Sema5a', 'mm10_Piezo2', 'mm10_Apoc1'),
            ('mm10_Eps8', 'mm10_Ces1c', 'hg19_DOCK4', 'mm10_Slc25a21', 'hg19_TRIT1', 'mm10_Ptk2b', 'hg19_TPR', 'hg19_RABGAP1L', 'mm10_Clasp1', 'mm10_Rapgef5', 'mm10_Nlrp1b'),
            ('mm10_Anxa2', 'mm10_C9', 'hg19_POLA1', 'mm10_Pigr', 'hg19_NONO', 'mm10_Pld4', 'hg19_OGT', 'hg19_DACH1', 'mm10_Nrros', 'mm10_Col1a1', 'mm10_Ikzf1'),
            ('mm10_Ncl', 'mm10_Gbe1', 'hg19_BPTF', 'mm10_Ptprk', 'hg19_PCNT', 'mm10_Csf1r', 'hg19_SREK1', 'hg19_INVS', 'mm10_Tnik', 'mm10_Maf', 'mm10_Ctsc'),
            ('mm10_Frmd4a', 'mm10_Iqgap2', 'hg19_GABRA1', 'mm10_Atp11c', 'hg19_FUS', 'mm10_Gpr141', 'hg19_DACH1', 'hg19_RERE', 'mm10_Grap2', 'mm10_Sh3rf1', 'mm10_Gbp8'),
            ('mm10_Ptk2', 'mm10_Slc7a2', 'hg19_NEAT1', 'mm10_Acox1', 'hg19_RPS27A', 'mm10_Pik3cd', 'hg19_RORA', 'hg19_COA1', 'mm10_Hip1', 'mm10_Rnf150', 'mm10_Ptgs1'),
            ('mm10_Ankrd11', 'mm10_Serpinc1', 'hg19_ANKRD36C', 'mm10_Apoc1', 'hg19_ZRANB2', 'mm10_Prkcb', 'hg19_YARS', 'hg19_PNN', 'mm10_Ubash3a', 'mm10_Tek', 'mm10_Snx24'),
            ('mm10_Large', 'mm10_Camk1d', 'hg19_UTRN', 'mm10_Lipc', 'hg19_ARID4B', 'mm10_Arid5b', 'hg19_EPHA6', 'hg19_NKTR', 'hg19_RP11-315L6.1', 'mm10_Ephb2', 'mm10_Pecam1'),
            ('mm10_Col5a1', 'mm10_Gphn', 'hg19_JPX', 'mm10_Pbld2', 'hg19_RPL36A', 'mm10_Mycbp2', 'hg19_AGAP1', 'hg19_SRSF4', 'mm10_Cnot6l', 'mm10_Ets1', 'mm10_Cmss1'),
            ('mm10_Myof', 'mm10_Ass1', 'hg19_ZNF736', 'mm10_Wwox', 'hg19_RPL26', 'mm10_Wdfy4', 'hg19_ARID4B', 'hg19_SREK1', 'mm10_B4galt5', 'mm10_Flt1', 'mm10_Ksr2'),
            ('mm10_Tanc2', 'mm10_Cyp4v3', 'hg19_RP11-514P8.6', 'mm10_Rabgap1l', 'hg19_EIF5B', 'mm10_Diaph2', 'hg19_WHSC1', 'hg19_SND1', 'mm10_Ets1', 'mm10_Rbpms', 'mm10_Nadk'),
            ('mm10_Anp32b', 'mm10_Cyp2j5', 'hg19_ANKHD1', 'mm10_Itih2', 'hg19_HSP90AB1', 'mm10_Naip2', 'hg19_TNRC6B', 'hg19_LARGE', 'hg19_RP11-144F15.1', 'mm10_Bmpr2', 'mm10_Tnfaip2'),
            ('mm10_Timp2', 'mm10_Gm26908', 'hg19_PHF14', 'mm10_Gm26917', 'hg19_WWOX', 'mm10_Lrch1', 'hg19_CDKAL1', 'hg19_PARD3B', 'mm10_Sp100', 'mm10_Foxp4', 'mm10_Cept1')],
           dtype=[('0', '<U50'), ('1', '<U50'), ('2', '<U50'), ('3', '<U50'), ('4', '<U50'), ('5', '<U50'), ('6', '<U50'), ('7', '<U50'), ('8', '<U50'), ('9', '<U50'), ('10', '<U50')]),
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            (1.11797730e-050, 6.57427864e-07, 6.95002766e-02, 9.39193181e-044, 5.90632608e-010, 6.11023102e-12, 9.69635278e-36, 9.41570820e-15, 5.82155137e-02, 2.91993414e-03, 4.06393151e-03),
            (1.59712844e-051, 9.20567527e-07, 7.62831088e-02, 6.46979912e-043, 6.46443722e-010, 5.64422820e-12, 2.56235451e-35, 1.29285214e-14, 5.88107708e-02, 3.15978450e-03, 4.14111692e-03),
            (1.11797730e-050, 1.12186335e-06, 7.70753476e-02, 7.09739223e-044, 7.92845827e-010, 7.49226395e-12, 1.43714276e-35, 1.34533642e-14, 5.99141132e-02, 3.26587344e-03, 4.32775557e-03),
            (3.35744552e-050, 1.30773735e-06, 8.28409742e-02, 1.82625818e-042, 1.14314657e-009, 1.10461774e-11, 1.04211997e-35, 1.78345700e-14, 5.96168682e-02, 3.47255844e-03, 4.06393151e-03),
            (8.39006654e-051, 1.54449509e-06, 8.36836756e-02, 1.17488289e-042, 9.93273166e-010, 8.87159330e-12, 1.45502829e-35, 1.31011325e-14, 6.14980425e-02, 3.96034025e-03, 4.99901942e-03),
            (5.18700385e-049, 1.65956961e-06, 8.38969538e-02, 1.70212923e-042, 1.02465681e-009, 2.96375570e-11, 3.34813915e-35, 2.21871595e-14, 6.31006072e-02, 4.19615337e-03, 6.27832901e-03),
            (6.41644825e-050, 1.77617746e-06, 8.61819379e-02, 2.01813110e-042, 1.37884749e-009, 3.38624294e-11, 2.88187642e-35, 2.38086254e-14, 6.39446631e-02, 4.11178547e-03, 7.51224895e-03),
            (1.21797919e-048, 1.99419680e-06, 8.94118122e-02, 1.63433989e-041, 1.96931722e-009, 3.03946588e-11, 9.98359661e-35, 2.56344245e-14, 6.74608011e-02, 4.19615337e-03, 7.59378920e-03),
            (3.32293232e-049, 2.01561118e-06, 9.03529089e-02, 1.34896714e-043, 2.50555324e-009, 3.38624294e-11, 9.20167022e-35, 2.42191939e-14, 6.66142182e-02, 4.11178547e-03, 8.60090092e-03),
            (2.36420608e-049, 2.37653300e-06, 9.14599084e-02, 1.67531072e-042, 2.39050198e-009, 4.33957022e-11, 6.64943161e-35, 2.56344245e-14, 6.72268589e-02, 4.19615337e-03, 9.87999015e-03),
            (1.11671349e-048, 2.62102278e-06, 9.20159015e-02, 2.59835872e-041, 3.30503653e-009, 5.69959790e-11, 7.72343759e-35, 4.01275903e-14, 6.83761629e-02, 4.90510864e-03, 9.84474268e-03),
            (5.48496673e-049, 2.95270972e-06, 9.20159015e-02, 1.63433989e-041, 3.57639691e-009, 6.14105637e-11, 7.69225901e-35, 3.89446259e-14, 6.98122136e-02, 4.35809933e-03, 9.57924910e-03),
            (1.34922095e-048, 3.02591876e-06, 1.00223321e-01, 2.15679595e-041, 3.72043358e-009, 9.35726197e-11, 1.70889053e-34, 4.43881296e-14, 7.02887440e-02, 5.29374080e-03, 1.41320804e-02),
            (2.02421128e-048, 3.00936532e-06, 1.00480752e-01, 1.92494133e-041, 4.00576264e-009, 9.84636104e-11, 1.52529030e-34, 4.54990691e-14, 7.04520827e-02, 5.02768035e-03, 1.22776606e-02),
            (1.43499887e-047, 3.88752701e-06, 1.01208148e-01, 1.20870446e-042, 5.11985624e-009, 7.74949561e-11, 1.72896279e-34, 4.43881296e-14, 7.29038447e-02, 5.44672057e-03, 1.39971706e-02),
            (5.54016026e-048, 4.37669319e-06, 1.03405706e-01, 1.23853526e-040, 5.70123469e-009, 7.14986791e-11, 2.06444006e-34, 8.65682668e-14, 7.65739671e-02, 6.00521980e-03, 1.44510906e-02),
            (2.46926321e-047, 4.82587769e-06, 1.03783153e-01, 3.98835717e-040, 6.36823406e-009, 1.27673358e-10, 2.19145560e-34, 9.53495546e-14, 7.79359436e-02, 5.29374080e-03, 1.53443499e-02),
            (5.54791616e-048, 5.15488995e-06, 1.04007727e-01, 5.77630180e-040, 6.40997000e-009, 4.22859985e-10, 2.50293744e-34, 8.63105911e-14, 8.49116958e-02, 5.85581148e-03, 1.50898293e-02),
            (3.75630974e-048, 5.76706423e-06, 1.05622421e-01, 4.54212353e-040, 5.91857742e-009, 4.22859985e-10, 2.51748239e-34, 9.53679243e-14, 8.48097786e-02, 7.64107739e-03, 1.86223778e-02),
            (2.46926321e-047, 7.58561728e-06, 1.06636559e-01, 2.48272964e-039, 5.93590531e-009, 5.09438602e-10, 6.69767140e-34, 1.12966451e-13, 9.08744355e-02, 7.60138126e-03, 1.94683633e-02),
            (4.86088827e-047, 7.67432293e-06, 1.10646593e-01, 2.28316782e-038, 7.30627326e-009, 7.23311154e-10, 6.04111784e-34, 1.22790895e-13, 9.69122407e-02, 7.60138126e-03, 1.98410869e-02),
            (6.01348186e-048, 8.48755873e-06, 1.10852275e-01, 3.12341683e-038, 7.18522528e-009, 7.65617059e-10, 1.31389499e-33, 1.12685661e-13, 1.04912093e-01, 8.06875464e-03, 2.42890440e-02),
            (3.45849555e-047, 8.52621387e-06, 1.11403168e-01, 1.72315351e-038, 7.24729702e-009, 8.75184988e-10, 1.08509808e-33, 1.17904388e-13, 1.04423290e-01, 8.14654960e-03, 2.42890440e-02),
            (2.14704960e-046, 8.61168106e-06, 1.12282580e-01, 1.85265988e-037, 7.46224506e-009, 7.22525121e-10, 4.82805009e-33, 1.22790895e-13, 1.04912093e-01, 9.23702811e-03, 2.52498904e-02),
            (5.98978944e-046, 1.01897901e-05, 1.12811029e-01, 1.39081948e-038, 8.63318152e-009, 1.30214555e-09, 1.18114779e-32, 1.42632763e-13, 1.05731576e-01, 9.03489514e-03, 2.73845914e-02),
            (4.81516530e-046, 1.19308181e-05, 1.13192749e-01, 1.53148001e-037, 8.84877688e-009, 1.42658295e-09, 8.04886488e-33, 1.94994634e-13, 1.18909114e-01, 9.21459797e-03, 2.85255834e-02),
            (1.43121005e-045, 1.29026118e-05, 1.16972371e-01, 6.77186240e-037, 9.05963484e-009, 1.80418116e-09, 1.53944666e-32, 1.74615865e-13, 1.20493277e-01, 9.94468477e-03, 3.12742981e-02),
            (5.28489103e-045, 1.40657810e-05, 1.17230857e-01, 9.36944036e-037, 1.57573736e-008, 1.55901128e-09, 1.13102652e-32, 2.19596550e-13, 1.24735361e-01, 1.29954735e-02, 3.18099791e-02),
            (3.50896086e-046, 1.53149913e-05, 1.17315427e-01, 4.80666137e-037, 1.23319066e-008, 1.54437253e-09, 3.15812513e-32, 1.92592771e-13, 1.40895155e-01, 1.38776005e-02, 3.14562183e-02),
            (2.22892767e-045, 1.63445457e-05, 1.17681021e-01, 4.10838764e-036, 1.35096264e-008, 1.31491748e-09, 1.63808716e-32, 2.22513603e-13, 1.40513720e-01, 1.52474586e-02, 3.51239077e-02),
            (6.81685567e-045, 1.82471031e-05, 1.19814302e-01, 4.67379251e-036, 1.83011035e-008, 1.49398225e-09, 2.01482490e-32, 2.44631791e-13, 1.39703816e-01, 1.50712972e-02, 3.60349475e-02),
            (5.28489103e-045, 1.90944491e-05, 1.21375086e-01, 1.85544476e-037, 1.94992857e-008, 2.22053847e-09, 5.46784212e-32, 2.37508511e-13, 1.40895155e-01, 1.56302241e-02, 3.94562740e-02),
            (4.13403897e-045, 2.00609708e-05, 1.21375086e-01, 2.05261830e-036, 1.83902475e-008, 1.80714004e-09, 5.45440934e-32, 3.21546072e-13, 1.42206001e-01, 1.56302241e-02, 3.97009348e-02),
            (3.27724326e-045, 2.03108311e-05, 1.22340660e-01, 1.82102646e-035, 2.03443629e-008, 2.58135808e-09, 5.63965369e-32, 3.53211049e-13, 1.43811212e-01, 1.58447818e-02, 4.12617251e-02),
            (2.18042636e-044, 2.23371038e-05, 1.22466356e-01, 4.17730816e-037, 1.82301326e-008, 2.05145819e-09, 8.05193393e-32, 5.10695519e-13, 1.45596119e-01, 1.57716702e-02, 4.44738557e-02)],
           dtype=[('0', '<f8'), ('1', '<f8'), ('2', '<f8'), ('3', '<f8'), ('4', '<f8'), ('5', '<f8'), ('6', '<f8'), ('7', '<f8'), ('8', '<f8'), ('9', '<f8'), ('10', '<f8')])}
In [1486]:
def rank_genes_groups_df(adata, group, pval_cutoff : float =None, logfc_cutoff=None): 
    d = pd.DataFrame() 
    for k in ['scores', 'names', 'logfoldchanges', 'pvals', 'pvals_adj']: 
        d[k] = adata.uns["rank_genes_groups"][k][group] 
    if pval_cutoff is not None: 
        d = d[d["pvals_adj"] < pval_cutoff] 
    if logfc_cutoff is not None: 
        d = d[d["logfoldchanges"].abs() > logfc_cutoff] 
    return d

de_df = rank_genes_groups_df(adata)

de_df.hvplot.scatter(
    "logfoldchanges", "pvals", 
    flip_yaxis=True, logy=True, 
    hover_cols=["names"]
)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1486-1d92893bb4d9> in <module>
      9     return d
     10 
---> 11 de_df = rank_genes_groups_df(adata)
     12 
     13 de_df.hvplot.scatter(

TypeError: rank_genes_groups_df() missing 1 required positional argument: 'group'
In [1002]:
adata.obs['barcode']=df['barcode']
In [ ]:
 
In [218]:
plate_map='../XYZeq/data/20181219_species_exp/plate23_map.csv'
spatial_map=pd.read_csv(plate_map, header=None, names=['barcode', 'X', 'Y'])
In [219]:
cutoff=0
In [220]:
def hexagon(x, y, c='k'):
	from matplotlib.patches import Polygon

	points = [[x + (2/3), y],
			  [x + (1/3), y + 0.5],
   			  [x - (1/3), y + 0.5],
              [x - (2/3), y],
   			  [x - (1/3), y - 0.5],
   			  [x + (1/3), y - 0.5]]
    
	polygon = Polygon(points, edgecolor='w', fc=c)
	return polygon
In [1467]:
df=pd.DataFrame(adata.obs.CellType )
In [ ]:
 
In [1468]:
df['barcode']=df.index.str[-16:]
In [1471]:
color_itorator=0



for t in adata.obs.CellType.cat.categories:
#for t in ['1']:
    df_=df[df['CellType'] == t]
    
    #sets cell counter
    df_['count']=1
    
    df__=df_.groupby('barcode', as_index=False)['count'].sum()

    map_array=spatial_map.merge(df__, on='barcode', how='left')
    
    map_array=map_array.fillna('0')
    map_array['count']=map_array['count'].astype(int)
    
    
    
    # max value for coloring
    max_val=max(map_array['count'])
    norm = mpl.colors.Normalize(vmin=0,vmax=max_val)
    
    custom_color=adata.uns['CellType_colors'][color_itorator]
    custom_map = mpl.colors.LinearSegmentedColormap.from_list("", ["#ffffff",custom_color])
    
    color_itorator = color_itorator+1
    
    count_hex=[]
    for i in map_array['count']:
        if i > cutoff:

            count_hex.append(mpl.colors.rgb2hex(custom_map(norm(i))[:3]))
        #    count_hex.append('#000000')
            
        else:
            count_hex.append('#ffffff')

    map_array['color']=count_hex
    
    for index, row in map_array.iterrows():
        if row['X'] % 2 != 0:
            row['Y']=row['Y']-0.5
    
        polygon = hexagon(row['X'],row['Y'],row['color'])
        plt.gca().add_patch(polygon)
    
  
    plt.ylim(18,0) #flip y axis
    plt.axis('scaled')
    plt.rcParams['figure.figsize'] = [15, 7.5] 
    
    plt.ylabel('cell number')
    plt.title('cluster %s' %t) 
    plt.axis('off')

    
    
    
    
#fix this here...
 #   mesh = plt.pcolormesh(pd.DataFrame(map_array['count']), cmap=custom_map)
 #   plt.colorbar(mesh)
    
 #   plt.savefig('/Users/derekbogdanoff/Desktop/test.svg', bbox_inches='tight')
    plt.show()
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  # This is added back by InteractiveShellApp.init_path()
In [1105]:
df=pd.DataFrame(adata.obs.cell_call )
In [1106]:
df['barcode']=df.index.str[-16:]
In [1107]:
#color_itorator=0

for t in adata.obs.cell_call.cat.categories:
#for t in ['1']:
    df_=df[df['cell_call'] == t]
    
    #sets cell counter
    df_['count']=1
    
    df__=df_.groupby('barcode', as_index=False)['count'].sum()

    map_array=spatial_map.merge(df__, on='barcode', how='left')
    
    map_array=map_array.fillna('0')
    map_array['count']=map_array['count'].astype(int)
    
    
    
    # max value for coloring
    max_val=max(map_array['count'])
    norm = mpl.colors.Normalize(vmin=0,vmax=max_val)
    
#     custom_color=adata.uns['CellType_colors'][color_itorator]
#     custom_map = mpl.colors.LinearSegmentedColormap.from_list("", ["#ffffff",custom_color])
    
#     color_itorator = color_itorator+1
    
    count_hex=[]
    for i in map_array['count']:
        if i > cutoff:

        #    count_hex.append(mpl.colors.rgb2hex(mpl.cm.plasma(norm(i))[:3]))
            count_hex.append('#000000')
            
        else:
            count_hex.append('#ffffff')

    map_array['color']=count_hex
    
    for index, row in map_array.iterrows():
        if row['X'] % 2 != 0:
            row['Y']=row['Y']-0.5
    
        polygon = hexagon(row['X'],row['Y'],row['color'])
        plt.gca().add_patch(polygon)
    
  
    plt.ylim(18,0) #flip y axis
    plt.axis('scaled')
    plt.rcParams['figure.figsize'] = [15, 7.5] 
    plt.title('cluster %s' %t) 
    plt.axis('off')
    
    
#fix this here...
   # mesh = plt.pcolormesh(pd.DataFrame(map_array['count']), cmap=mpl.cm.plasma)
    #plt.colorbar(mesh)
    
 #   plt.savefig('/Users/derekbogdanoff/Desktop/test.svg', bbox_inches='tight')
    plt.show()
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  
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In [84]:
for t in adata_filtered.obs.leiden.unique():        #for each unique feature in adata.obs
    df_=df[df['leiden'] == t]
    df_['count']=1
    
    df__=df_.groupby('barcode', as_index=False)['count'].sum()
#    df__.columns=['barcode','count']
    map_array=spatial_map.merge(df__, on='barcode', how='left')
    
    map_array=map_array.fillna('0')
    map_array['count']=map_array['count'].astype(int)
    
    max_val=max(map_array['count'])
    norm = mpl.colors.Normalize(vmin=0,vmax=max_val)
    
    
 #   cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","#1f77b4"])
    
    
    
    
    count_hex=[]
    for i in map_array['count']:
        if i > cutoff:

            count_hex.append(mpl.colors.rgb2hex(cm.Greys(norm(i))[:3]))
            #count_hex.append('#E6E6FA')
            
        else:
            count_hex.append('#ffffff')

    map_array['color']=count_hex
    
    
    
    
    
    
    for index, row in map_array.iterrows():
        if row['X'] % 2 != 0:
            row['Y']=row['Y']-0.5
    
        polygon = hexagon(row['X'],row['Y'],row['color'])
        plt.gca().add_patch(polygon)
    
  
    plt.ylim(18,0) #flip y axis
    plt.axis('scaled')
    plt.rcParams['figure.figsize'] = [15, 7.5] 
    plt.title('leiden cluster %s' %t) 
    plt.axis('off')
    
 #   plt.colorbar()
    
    plt.show()
/Users/derekbogdanoff/projects/venv3/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  This is separate from the ipykernel package so we can avoid doing imports until
In [ ]:
 
In [279]:

Out[279]:
<matplotlib.colors.ListedColormap at 0x13c1f4518>
In [966]:
_df=df.merge(spatial_map,on='barcode',how='right')

_df['count']=1

_df['CellType']=_df['CellType'].cat.add_categories('None').fillna('None')
_df['count']=_df['count'].fillna(int(1))

df_=_df.groupby(['barcode','CellType'])['count'].sum()

df__=pd.DataFrame(df_)
In [968]:
adata.uns['CellType_colors']
Out[968]:
['#b3b3b3', '#fc8d62', '#8da0cb', '#a6d854', '#ffd92f', '#e78ac3', '#66c2a5']
In [1014]:
# #CellType Color_dict
# color_dict={'Hek293t':'#b3b3b3', 
#             'Hepatocyte':'#fc8d62',
#             'Kupffer':'#8da0cb',
#             'LSEC':'#a6d854',
#             'Lymphocyte':'#ffd92f',
#             'Macrophage':'#e78ac3',
#             'mc38':'#66c2a5',
#             'None':'#ffffff'}




color_dict={'Hek293t':'#e41a1c', 
            'Hepatocyte':'#377eb8',
            'Kupffer':'#984ea3',
            'LSEC':'#ff7f00',
            'Lymphocyte':'#ffff33',
            'Macrophage':'#f781bf',
            'mc38':'#4daf4a',
            'None':'#ffffff'}





# #Cell_call color dict
# color_dict={'M':'orangered', 
#             'H':'royalblue',
#             'C':'lightgrey',
#             'None':'#ffffff',}
In [1015]:
fig8 = plt.figure(constrained_layout=False)



gs1 = fig8.add_gridspec(nrows=36, ncols=44, left=-0.05, right=0.48, hspace=-0.98,wspace=-.21)


for index, row in spatial_map.iterrows():
    x_coordinate = row['X']-1
    
    if (row['X']-1)  % 2 == 0:
        y_coordinate = ((row['Y']-1)*2)
    else:
        y_coordinate = ((row['Y']*2)-1)
    
    
    
    barcode=str(row['barcode'])
    
    
    data=df__.loc[barcode]
    counts=data['count'].tolist()
    
    
    #print([y_coordinate, x_coordinate])
    
    
    color_list=[]
    for i in data.index.tolist():
        color_list.append(color_dict[i])
    
    
    ax = fig8.add_subplot(gs1[y_coordinate,x_coordinate])
    ax.pie(counts, colors=color_list)
    
    
  #  df__.loc[barcode].plot.pie('count')
fig8.savefig('/Users/derekbogdanoff/Desktop/test.svg', bbox_inches='tight')
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In [ ]:
 
In [1089]:
_df=df.merge(spatial_map,on='barcode',how='right')

_df['count']=1

_df['CellType']=_df['CellType'].cat.add_categories('None').fillna('None')
_df['count']=_df['count'].fillna(int(1))

df_=_df.groupby(['barcode','CellType'])['count'].sum()

df__=pd.DataFrame(df_)
In [1090]:
def rand_jitter(arr):
    stdev = .005*(max(arr)-min(arr))
    return arr + np.random.randn(len(arr)) * stdev
In [1091]:
#_df
In [1092]:
list_=[]

for index, row in _df.iterrows():
    if row['X'] % 2 != 0:
        row['Y']=row['Y']-0.5
    else:  
        row['Y']=row['Y']
        
    list_.append(row['Y'])
        
In [1093]:
_df['Y']=list_
In [1094]:
_df['Y']=_df['Y']/2
In [ ]:
 
In [1462]:
test=_df
In [ ]:
 
In [1463]:
test['color']=test['CellType'].map(color_dict)
In [ ]:
 
In [ ]:
 
In [1464]:
test['X']=rand_jitter(test['X'])
test['Y']=rand_jitter(test['Y'])
In [1465]:
test.plot.scatter(x='X',y='Y',c=test['color'], s=60,alpha=.6)
plt.ylim(18,0)

plt.axis('off')
plt.grid(b=None)
In [1080]:
test[test['CellType']=='mc38'].plot.scatter(x='X',y='Y',c=test[test['CellType']=='mc38']['color'], s=60)
plt.ylim(18,0)

plt.axis('off')
plt.grid(b=None)
In [1029]:
test[test['CellType']=='Hepatocyte'].plot.scatter(x='X',
                                                  y='Y',
                                                  c=test[test['CellType']=='Hepatocyte']['color'],
                                                  s=50)
plt.ylim(18,0)

plt.axis('off')
plt.grid(b=None)
In [1030]:
test[test['CellType']=='Hek293t'].plot.scatter(x='X',
                                           y='Y',
                                               c=test[test['CellType']=='Hek293t']['color'],
                                               s=50)
plt.ylim(18,0)

plt.axis('off')
plt.grid(b=None)
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In [ ]:

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